Thanks to the flexibility of RL, it can be employed not only in standalone tasks but also as a sort of fine-tune method in supervised learning algorithms. In many natural language processing (NLP) and computer vision tasks, the metric to optimize isn't differentiable, so to address the problem in supervised settings with neural networks, it needs an auxiliary differentiable loss function. However, the discrepancy between the two loss functions will penalize the final performance. One way to deal with this is to first train the system using supervised learning with the auxiliary loss function, and then use RL to fine-tune the network optimizing with respect to the final metric. For instance, this process can be of benefit in subfields such as machine translation and question answering, where the evaluation metrics are complex and not differentiable.

Furthermore, RL can solve NLP problems such as dialogue systems and text generation. Computer vision, localization, motion analysis, visual control, and visual tracking can all be trained with deep RL.

Deep learning proposes to overcome the heavy task of manual feature engineering while requiring the manual design of the neural network architecture. This is tedious work involving many parts that have to be combined in the best possible way. So, why can we not automate it? Well, actually, we can. Neural architecture design (NAD) is an approach that uses RL to design the architecture of deep neural networks. This is computationally very expensive, but this technique is able to create DNN architectures that can achieve state-of-the-art results in image classification.